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3pia
LBN
Commits
5ba4cb85
Commit
5ba4cb85
authored
Apr 13, 2019
by
Marcel Rieger
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Merge branch 'dev_tf2' into 'master'
Add support for TensorFlow v2. See merge request
!1
parents
ead19cfb
b2806cfd
Pipeline
#119164
passed with stages
in 39 seconds
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4 changed files
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119 additions
and
23 deletions
+119
-23
.gitlab-ci.yml
.gitlab-ci.yml
+18
-2
README.md
README.md
+25
-7
lbn.py
lbn.py
+33
-9
test.py
test.py
+43
-5
No files found.
.gitlab-ci.yml
View file @
5ba4cb85
...
...
@@ -12,7 +12,7 @@ lint:
-
pip install flake8 --user
-
flake8 lbn.py test.py setup.py
unittest
s
_py2
:
unittest
_tf1
_py2
:
stage
:
test
tags
:
-
docker
...
...
@@ -20,10 +20,26 @@ unittests_py2:
script
:
-
python -m unittest test
unittests_py3
:
unittests_
tf1_
py3
:
stage
:
test
tags
:
-
docker
image
:
tensorflow/tensorflow:1.13.1-py3
script
:
-
python -m unittest test
unittest_tf2_py2
:
stage
:
test
tags
:
-
docker
image
:
tensorflow/tensorflow:2.0.0a0
script
:
-
python -m unittest test
unittests_tf2_py3
:
stage
:
test
tags
:
-
docker
image
:
tensorflow/tensorflow:2.0.0a0-py3
script
:
-
python -m unittest test
README.md
View file @
5ba4cb85
...
...
@@ -8,6 +8,7 @@ Original repository: [git.rwth-aachen.de/3pia/lbn](https://git.rwth-aachen.de/3p
### Usage example
```
python
import
tensorflow
as
tf
from
lbn
import
LBN
# initialize the LBN, set 10 combinations and pairwise boosting
...
...
@@ -20,6 +21,25 @@ features = lbn(four_vectors)
...
```
Or with TensorFlow 2 and Keras:
```
python
import
tensorflow
as
tf
from
lbn
import
LBN
,
LBNLayer
# start a sequential model
model
=
tf
.
keras
.
models
.
Sequential
()
# add the LBN layer
model
.
add
(
LBNLayer
(
10
,
boost_mode
=
LBN
.
PAIRS
))
# add a dense layer
model
.
add
(
tf
.
keras
.
layers
.
Dense
(
1024
))
# continue builing and training the model
...
```
### Installation and dependencies
...
...
@@ -29,23 +49,21 @@ Via [pip](https://pypi.python.org/pypi/lbn):
pip
install
lbn
```
NumPy and TensorFlow (1.X) are the only dependencies.
TensorFlow 2.0 is not
*yet*
supported.
NumPy and TensorFlow are the only dependencies. Both TensorFlow v1 and v2 are supported.
### Testing
Tests should be run for Python 2 and 3. The following commands assume you are root directory of the LBN respository:
Tests should be run for Python 2 and 3
and for TensorFlow 1 and 2
. The following commands assume you are root directory of the LBN respository:
```
bash
python
-m
unittest
test
# or via docker, python 2
# or via docker, python 2
and tf 1
docker run
--rm
-v
`
pwd
`
:/root/lbn
-w
/root/lbn tensorflow/tensorflow:1.13.1 python
-m
unittest
test
# or via docker, python 3
docker run
--rm
-v
`
pwd
`
:/root/lbn
-w
/root/lbn tensorflow/tensorflow:
1.13.1
-py3 python
-m
unittest
test
# or via docker, python 3
and tf 2
docker run
--rm
-v
`
pwd
`
:/root/lbn
-w
/root/lbn tensorflow/tensorflow:
2.0.0a0
-py3 python
-m
unittest
test
```
...
...
lbn.py
View file @
5ba4cb85
...
...
@@ -13,7 +13,7 @@ __contact__ = "https://git.rwth-aachen.de/3pia/lbn"
__email__
=
"marcel.rieger@cern.ch"
__version__
=
"1.0.3"
__all__
=
[
"LBN"
,
"FeatureFactoryBase"
,
"FeatureFactory"
]
__all__
=
[
"LBN"
,
"
LBNLayer"
,
"
FeatureFactoryBase"
,
"FeatureFactory"
]
import
os
...
...
@@ -118,6 +118,9 @@ class LBN(object):
else
:
raise
ValueError
(
"invalid batch_norm, should be bool or list/tuple of two bools"
)
# the keras batch normalization layer
self
.
batch_norm
=
None
# particle weights and settings
self
.
particle_weights
=
particle_weights
self
.
abs_particle_weights
=
abs_particle_weights
...
...
@@ -210,7 +213,7 @@ class LBN(object):
if
self
.
features
is
None
:
return
None
return
self
.
features
.
shape
[
-
1
].
value
return
int
(
self
.
features
.
shape
[
-
1
])
def
register_feature
(
self
,
func
=
None
,
**
kwargs
):
"""
...
...
@@ -300,8 +303,8 @@ class LBN(object):
self
.
inputs
=
inputs
# infer sizes
self
.
n_in
=
self
.
inputs
.
shape
[
1
].
value
self
.
n_dim
=
self
.
inputs
.
shape
[
2
].
value
self
.
n_in
=
int
(
self
.
inputs
.
shape
[
1
])
self
.
n_dim
=
int
(
self
.
inputs
.
shape
[
2
])
if
self
.
n_dim
!=
4
:
raise
Exception
(
"input dimension must be 4 to represent 4-vectors"
)
...
...
@@ -509,16 +512,37 @@ class LBN(object):
def
build_norm
(
self
):
"""
Applies simple batch normalization with floating averages to the output features using
``tf.layers.batch_normalization``. Make sure to also run the operation returned by
``tf.get_collection(tf.GraphKeys.UPDATE_OPS)`` during each train step.
``tf.keras.layers.BatchNormalization``.
"""
self
.
_norm_features
=
tf
.
layers
.
batch_normalization
(
self
.
features
,
self
.
batch_norm
=
tf
.
keras
.
layers
.
BatchNormalization
(
axis
=
1
,
training
=
self
.
is_training
,
center
=
self
.
batch_norm_center
,
scale
=
self
.
batch_norm_scale
,
)
self
.
_norm_features
=
self
.
batch_norm
(
self
.
features
,
training
=
self
.
is_training
)
class
LBNLayer
(
tf
.
keras
.
layers
.
Layer
):
"""
Keras layer of the :py:class:`LBN` that forwards the standard interface of :py:meth:`__init__`
and py:meth:`__call__`.
.. py:attribute:: lbn
type: LBN
Reference to the internal :py:class:`LBN` instance that is initialized with the contructor
arguments of this class.
"""
def
__init__
(
self
,
*
args
,
**
kwargs
):
super
(
LBNLayer
,
self
).
__init__
()
# create the LBN instalce
self
.
lbn
=
LBN
(
*
args
,
**
kwargs
)
def
__call__
(
self
,
*
args
,
**
kwargs
):
# forward to lbn.__call__
return
self
.
lbn
(
*
args
,
**
kwargs
)
class
FeatureFactoryBase
(
object
):
...
...
test.py
View file @
5ba4cb85
...
...
@@ -8,15 +8,20 @@ LBN unit tests.
__all__
=
[
"TestCase"
]
import
sys
import
unittest
import
numpy
as
np
import
tensorflow
as
tf
from
lbn
import
LBN
,
FeatureFactory
from
lbn
import
LBN
,
LBNLayer
,
FeatureFactory
# enable eager execution
tf
.
enable_eager_execution
()
PY3
=
sys
.
version
.
startswith
(
"3."
)
TF2
=
tf
.
__version__
.
startswith
(
"2."
)
if
not
TF2
:
tf
.
enable_eager_execution
()
class
TestCase
(
unittest
.
TestCase
):
...
...
@@ -24,8 +29,15 @@ class TestCase(unittest.TestCase):
def
__init__
(
self
,
*
args
,
**
kwargs
):
super
(
TestCase
,
self
).
__init__
(
*
args
,
**
kwargs
)
# fixate random seeds
np
.
random
.
seed
(
123
)
if
TF2
:
tf
.
random
.
set_seed
(
123
)
else
:
tf
.
random
.
set_random_seed
(
123
)
# create some four-vectors with fixed seed and batch size 2
self
.
vectors
=
create_four_vectors
((
2
,
10
)
,
seed
=
123
)
self
.
vectors
=
create_four_vectors
((
2
,
10
))
self
.
vectors_t
=
tf
.
constant
(
self
.
vectors
,
dtype
=
tf
.
float32
)
# common feature set
...
...
@@ -111,7 +123,7 @@ class TestCase(unittest.TestCase):
"particles_px"
,
"particles_py"
,
"particles_pz"
,
"particles_pvec"
,
"particles"
,
"restframes_E"
,
"restframes_px"
,
"restframes_py"
,
"restframes_pz"
,
"restframes_pvec"
,
"restframes"
,
"Lambda"
,
"boosted_particles"
,
"_raw_features"
,
"_norm_features"
,
"features"
,
"features"
,
"batch_norm"
,
]
lbn
=
LBN
(
10
,
boost_mode
=
LBN
.
PAIRS
,
is_training
=
True
)
...
...
@@ -315,6 +327,32 @@ class TestCase(unittest.TestCase):
# test the custom feature
self
.
assertAlmostEqual
(
lbn
.
feature_factory
.
px_plus_py
().
numpy
()[
1
,
0
],
-
36.780174
,
4
)
def
test_keras_layer
(
self
):
l
=
LBNLayer
(
10
,
boost_mode
=
LBN
.
PAIRS
,
batch_norm
=
True
,
is_training
=
True
)
self
.
assertIsInstance
(
l
.
lbn
,
LBN
)
self
.
assertTrue
(
l
.
lbn
.
batch_norm_center
)
# build a custom model
class
Model
(
tf
.
keras
.
models
.
Model
):
def
__init__
(
self
):
super
(
Model
,
self
).
__init__
()
self
.
lbn
=
l
self
.
dense
=
tf
.
keras
.
layers
.
Dense
(
1024
,
activation
=
"elu"
)
self
.
softmax
=
tf
.
keras
.
layers
.
Dense
(
2
,
activation
=
"softmax"
)
def
__call__
(
self
,
*
args
,
**
kwargs
):
return
self
.
softmax
(
self
.
dense
(
self
.
lbn
(
*
args
,
**
kwargs
)))
model
=
Model
()
output
=
model
(
self
.
vectors_t
,
features
=
self
.
feature_set
).
numpy
()
self
.
assertAlmostEqual
(
output
[
0
,
0
],
0.548664
if
PY3
else
0.795995
,
5
)
self
.
assertAlmostEqual
(
output
[
0
,
1
],
0.451337
if
PY3
else
0.204005
,
5
)
self
.
assertAlmostEqual
(
output
[
1
,
0
],
0.394629
if
PY3
else
0.177576
,
5
)
self
.
assertAlmostEqual
(
output
[
1
,
1
],
0.605371
if
PY3
else
0.822424
,
5
)
def
create_four_vectors
(
n
,
p_low
=-
100.
,
p_high
=
100.
,
m_low
=
0.1
,
m_high
=
50.
,
seed
=
None
):
"""
...
...
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